People have been discussing Artificial Intelligence (AI) since sci-fi films like Terminator and The Matrix.
Yet, although it has been around for a long time, many don't grasp its full meaning.
Common definitions of AI make things way more complex than they have to be.
So, we'll try to define AI by using simple terms to help you understand its role in our everyday lives. Let's dive right into it!
What is Artificial Intelligence (AI)?
Simply put, AI focuses on the ability of machines to mimic human cognitive capacities.
As a result, these systems don't require human intervention to complete complex tasks.
Common examples include AI-powered virtual assistants like Siri, Alexa, or Google Home.
These can understand human language and respond to questions to make informed decisions.
Other AI applications include image recognition, fraud detection and recommendations for content, songs and products.
In fact, modern tools like ChatGPT and Gemini (Google Bard) can respond remarkably similarly to the way we do it!
But AI tools can do things that we simply can't, like processing vast amounts of data or making videos in a matter of minutes.
However, that doesn't mean they can replace the worth of human intelligence and work!
AI tools can only work on the specific tasks they were trained for, and they can still make some mistakes from time to time.
Types of Artificial Intelligence
Artificial Narrow Intelligence (ANI)
"Weak" or "narrowed" AI works only on a specific task, which can sometimes perform better than we would.
Yet, ANI, or weak AI, don't possess any sort of awareness. It only works on what it's trained to do and nothing else.
Common tasks include Natural Language Generation— creating realistic images and understanding human speech.
Examples of ANI involve tools such as Alexa, Siri, ChatGPT Gemini and Midjourney.
Artificial General Intelligence (AGI)
AGI, or "strong" AI, is closer to the kind of AI we've seen in films or TV series.
Unlike ANI, AGI could potentially solve a wide range of problems and even understand human feelings and emotions.
Picture a robot or a machine capable of cooking dinner, solving math problems or even changing a flat tire.
Examples could resemble robots like TARS in Interstellar and "Joi" and the "Replicants" in Blade Runner.
It's worth noting that despite the fact we haven't achieved AGI yet, several large companies are working on it.
Artificial Super Intelligence (ASI)
In theory, ASI systems would be able to reason, judge, and possess cognitive capabilities that surpass ours.
These systems would also be able to improve on their own and set their goals and strategies to achieve them.
They would theoretically be able to mimic emotions and beliefs and use social skills to interact with us.
Since ASI is just hypothetical, the only examples we can give come from movies like Droids in Star Wars.
Tech corporations like IBM argue that some current AI use cases are the building blocks to achieving ASI in the future.
Examples include Large Language Models, Neural Networks, Conversational AI, self-driving cars and AI-generated programming.
How does AI Work in Simple Terms?
Computer programs work by executing a set of instructions (algorithms) to complete a specific task.
AI systems take this further by learning what the desired outcome is with examples of what's right and wrong.
These systems learn through trial and error, making it very similar to how we learned.
Models receive massive amounts of data and use algorithms that recognize patterns to reach the desired outcome.
The more data or information the AI models receive, the better the answers they can provide!
Once AI models become familiar with a topic, they can recognize more complex patterns and make accurate predictions.
That's why it has become popular in industries like healthcare, finances and weather forecasts.
Machines learn from data by leveraging the AI subfield called Machine Learning.
For instance, Data Scientists use Deep Learning to process larger amounts of data more efficiently.
These DL algorithms are used to create Neural Networks, which work as layers that process and categorize data.
How Does Machine Learning Work?
Data Scientists can build ML models by using different Machine Learning subfields.
One of these is Unsupervised Learning, used to analyze and identify similarities and patterns in data.
As a result, AI models can know what's right and what's wrong.
Think of a program that receives data from patients who have a particular disease.
By identifying similarities and patterns in data of patients with the disease, it can make data-driven decisions.
In Supervised Learning, the second approach, Data Scientists use two sets of data.
Following our example, one data set could be on healthy patients and the other on patients with the disease.
The program could analyze the frequency of specific conditions that sick patients have in common.
With this info, it could learn to predict when patients may have the disease.
The last approach, Reinforcement Learning, is particularly useful for programs dealing with unfamiliar scenarios.
A promising example would be how patients react to a certain medication.
This scope can help creating personalized treatments like Dynamic Treatment Regimes (DTRs).
Please note that the field is still under study as of yet.
Other common examples of Reinforcement Learning are in robotics and the gaming industry, with companies like Covariant and DeepMind.
Why is AI Important?
AI has become very important to multiple industries, including healthcare, gaming, finances and robotics.
Within AI, ML allows human workers in automating repetitive tasks, spam detection and facial recognition.
Teams are also leveraging it in Natural Language Processing and customer service with human-like text.
Popular Customer Relationship Management (CRM) platforms, such as Hubspot and Salesforce, have also embraced AI.
In supply chains, business models also use Generative AI to help forecast demand and improve customer engagement.
Financial institutions can also use AI to detect suspicious activities in real time and make predictions.
Lastly, Social Media Platforms and streaming platforms have included ML-built recommendation systems.
These features have proven efficient in increasing customer satisfaction and customer experience.
Conclusion
While AI-powered systems are very useful, human labor is invaluable in building trust and engagement with customers!
As a UX-driven Product Development agency, we've worked on several projects involving AI with clients like Sylvester.
Feel free to reach out if you'd like to know more about our approach to bringing business ideas to life.
